Under high-quality assurance programs it may be desirable to produce a design of an integrated circuit (IC) with very few defects, such as fewer than 3.4 defective parts per million with a Six Sigma quality program. To do so, process conditions across the operational spectrum must be sufficiently evaluated to determine IC robustness in rare conditions.
One way to evaluate an IC is to simulate the IC using an IC model. An IC model may describe an IC in terms of the number and types of components, the electrical connectivity of the components and the response of these components to electrical stimuli. One may use such an IC model to predict the electrical performance of an IC design. Using Monte Carlo techniques, assumed process conditions that vary may be repeatedly applied to an IC model to determine expected results. The variations imposed on the model are designed to comply with known probability distribution functions of the process or processes being varied.
Typically, the distribution of process parameters is a Gaussian distribution. Repeated simulations may require a significant amount of computational time because of the complexity of the models, the large number of components of a design being simulated, and the need to examine low-probability tails of the distribution to ensure a desired yield. By definition, the tails of a statistical distribution have low probabilities of occurrence. Thus, obtaining a high confidence estimate of these low probabilities may require running a large number of simulations.